Image analysis by object addition and recovery

ABSTRACT

The invention described herein is generally directed to methods for analyzing an image. In particular, crowded field images may be analyzed for unidentified, unobserved objects based on an iterative analysis of modified images including artificial objects or removed real objects. The results can provide an estimate of the completeness of analysis of the image, an estimate of the number of objects that are unobserved in the image, and an assessment of the quality of other similar images.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 13/902,097, filed May 24, 2013, which is acontinuation of U.S. patent application Ser. No. 13/423,803, filed Mar.19, 2012, which issued as U.S. Pat. No. 8,452,049 on May 28, 2013, whichis a continuation of U.S. patent application Ser. No. 13/074,465, filedMar. 29, 2011, which issued as U.S. Pat. No. 8,155,382 on Apr. 10, 2012,which is a continuation of U.S. patent application Ser. No. 11/852,614,filed Sep. 10, 2007, which issued as U.S. Pat. No. 7,940,959 on May 10,2011, which claims priority to and benefit of U.S. ProvisionalApplication No. 60/825,017 entitled “Classification Assessment Modelingby Simulated Target Addition and Recovery” filed on Sep. 8, 2006, thecontents of which is hereby incorporated by reference in its entirety.

GOVERNMENT INTERESTS

The United States Government may have certain rights to this inventionpursuant to work funded thereby under grants from the National ScienceFoundation (NSF) Small Business Technology Transfer Program (STTR)Contract No.: DMI-0441639, Topic IT.c9 “Information-based Technologies”.

PARTIES TO A JOINT RESEARCH AGREEMENT

Not applicable

INCORPORATION BY REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable

BACKGROUND

1. Field of Invention

The invention presented herein provides methods for analyzing images.

2. Description of Related Art

The outstanding characteristics of modern light detectors have made itpossible to retrieve high quality images from a wide variety ofplatforms. However, their high-fidelity nature does not help indisentangling the images of objects blended together in crowded fields,those that are partially obscured or barely visible in the noise of lowsignal-to-noise (SNR) data, or adversarial efforts to purposefullydeceive image detectors. Although certain indicators of quality arereadily apparent, such as pixilated objects that are unresolved, obviousblurriness, or uncertainty in visual characteristics due to low SNR,obtaining a realistic understanding of the limitations of image data isoften extremely difficult. Failing to detect an object does not provideenough data to be certain that the object is not actually present.

One approach to analyzing crowded fields in astronomy is artificial staranalysis. In this approach, a light pattern appropriate for a star of agiven brightness, color and location in the sky is added to thedigitized image data of a real star field. The modified data are thenanalyzed in the usual way, and the parameters derived from theartificial star are compared to the known input parameters. This processis then repeated thousands of times for stars with randomly chosencharacteristics. The deviations between the output of the analysisprogram and the known characteristics of the artificial stars are thenused to evaluate the relation between the results for the real stars andthe true underlying stellar populations in the field.

There are several limitations inherent in this method. The physics oftarget/background interaction in terrestrial images is more complex thanartificial star insertion into an astronomical image because terrestrialobjects are typically more complex than stellar profiles and becauseterrestrial backgrounds are considerably more complex than therelatively smooth background of space. Additionally, only a fewartificial stars may be added to the image data at any one time,otherwise the artificial stars themselves significantly change thecrowding of the field and the results become unreliable. Thus, a fulldata set (plus artificial stars) must be analyzed many, potentiallythousands of times using, for example, Monte Carlo simulation, to obtaina large statistical sample of artificial stars. Conceptually, theartificial star population must be comparable to the population of realstars, otherwise biases will be introduced. However, the population ofreal stars is not known in advance, so ensuring that the artificialstars are truly comparable to real stars requires considerableingenuity. Additionally, there are a variety of problems of detail, suchas the best way to characterize the light distribution that would becreated by an individual star. Moreover, determining the validity ofeach round of artificial star analysis is labor intensive and timeconsuming. Historically, each new round of artificial star analysis waspreceded by an astronomer evaluating the previously acquired data set todetermine if the resulting implied confidence-level was achieved. Theastronomer would then decide whether additional Monte Carlo simulationswere necessary.

Benefits of artificial star analysis include determination of detectionprobabilities across a range of observing conditions (light-level,background/foreground image structure, high-noise, etc.).

BRIEF SUMMARY OF THE INVENTION

Embodiments of the invention described herein include a method fordetecting an object in an image, the method including the steps of:obtaining one or more real images; analyzing the one or more real imagesand deriving a set of parameters defining each of the one or more realimages; creating a modified image; analyzing the modified image toderive a set of parameters defining the modified image; comparing theset of parameters derived from the modified image with the set ofparameters derived from the one or more real images; determining thelikelihood of additional objects being present within the one or morereal images; repeating the steps of creating, analyzing, comparing anddetermining; stopping the steps of creating, analyzing, comparing anddetermining when the probability of additional, unidentified objectsbeing present in the one or more real images has met a predeterminedlevel; and generating a report describing compiled results of repeatingthe steps of inserting, analyzing, comparing and determining.

In various embodiments, the step of creating a modified image mayinclude inserting at least one artificial object into at least one ofthe one or more real images or removing at least one object in at leastone of the one or more real images, and in some embodiments, creating amodified image may include a combination of inserting and removingobjects in the real image

The one or more real images used in the method may vary amongembodiments and may include digital images, analog images and acombination thereof obtained from aerial still images, aerial videoimages, satellite still images, satellite video images, ground to groundstill images, ground to ground video images, ground to air still images,ground to air video images, ground to water still images, ground towater video images, water to ground still images, water to ground videoimages, water to water still images, water to water video images, air toair still images, air to air video images or a combination thereof. Thearea encompassed by the one or more image may also vary amongembodiments and may include a terrestrial area, an aquatic area, anaerial area, an extraterrestrial area or a combination thereof.

The step of analyzing may be carried out using any process known in theart, and in some embodiments, the step of analyzing the one or more realimages and the step of analyzing the modified image are carried outusing the same process. In various embodiments, the step of analyzingthe one or more real image, the modified image or a combination thereofand deriving a set of parameters may further include determining a datavalue for parameters, such as, for example, a number of objects,brightness, contrast, color, shape, orientation, location and acombination thereof.

In various embodiments, the step of analyzing may further includedigitizing the one or more real images, digitizing the modified image orcombinations thereof. In certain embodiments, the step of analyzingfurther include creating a plurality of image chips, each of theplurality of image chips having a portion of the one or more real imagesor modified images and the step of analyzing may include parsing theplurality of image chips at least into one or more groups of image chipshaving objects and one or more groups of image chips not having objects.

In various embodiments, the steps of obtaining, inserting, analyzing ora combination thereof may further include enhancing at least a portionof the one or more real images or modified image by a method including,but not limited to, adjusting contrast, adjusting color, imageextraction, collaging, registering, coadding, averaging, median-filtercombining, sigma-clip averaging, splicing, histogram matching,mosaicking, convolution filtering, deconvolution filtering,unsharp-masking, edge detection, Fourier transformation, reducingbackground noise, texture processing and a combination thereof.

In some embodiments, the method may further include identifying at leasta portion of an object in the real image, and the step of identifying atleast a portion of an object may further include determining a totalnumber of objects in an image or a portion of an image. In otherembodiments, the step of identifying objects may further includecomparing the identified object to known objects to identify a type ofobject. The step of identifying an object may occur at any point in themethod, however, in certain embodiments, the step of identifying objectsmay occur concurrently with or following the step of analyzing.

In various embodiments, at least one artificial object added to an imagemay include at least a portion of an object identified in the one ormore real images, at least a portion of a known object, at least aportion of an archived object or a combination thereof. In otherembodiments, the at least a portion of the at least one artificialobject may include a set of parameters selected from brightness,contrast, color, shape, orientation, location and a combination thereof,wherein all or a subset of parameters are the same as a derived set ofparameters defining at least a portion of the one or more real image.

In some embodiments, the step of inserting at least one artificialobject may include rotating, scaling, shearing, smearing, rotating,convoluting, degrading or a combination thereof of the at least oneartificial object such that the at least one artificial object isconsistent within the context of the image, and in other embodiments,the step of inserting at least one artificial object further comprisesmodifying the at least one artificial object using a method such as, butnot limited to, adding shadows, adding one or more obscuration, adding alayover, performing a multipath, adding indirect illuminations, addingreduced/partial transparency effect, simulating camouflage, simulatingnetting, simulating vegetation, simulating ground covering, simulatingwater covering, simulating cloud covering, simulating weather,convolving with a nearby object and a combination thereof.

In some embodiments, the method may include recovering an opticalcharacteristic from the at least one modified image wherein the opticalcharacteristic comprises a property of an object selected frombrightness, color, location, orientation, reflectivity, a probability ofrecovering an optical characteristic, an uncertainty for the opticalcharacteristic recovered and a combination thereof. In particularembodiments, the method may include the step of applying the recoveredoptical characteristic to one or more objects of the one or more realimages, applying the recovered optical characteristic to one or morearchived object in a library, applying the recovered opticalcharacteristic to one or more real or modified images being analyzed,applying the recovered optical characteristic to one or more real ormodified images that are subsequently analyzed, or a combinationthereof.

In various embodiments, the identified object, artificial object orcombination thereof may be a human, an animal, a building, a machine, ageological formation, a type of plant, an aquatic feature, an aerialfeature, an airplane or airplanes, an extraterrestrial feature, avehicle, a military implement, an artillery installation, a tank and acombination thereof.

The method of some embodiments may also include the step of determininga confidence level for the one or more real images wherein theconfidence level is the probability of a number of objects in the one ormore original images being identified.

In various embodiments, the method may be automated.

Various embodiments, may also encompass a method for detecting an objectin an image including obtaining one or more real images; analyzing theone or more real images and deriving a set of parameters defining eachof the one or more real images; inserting at least one artificial objectinto at least one of the one or more real images to create a modifiedimage; analyzing the modified image to derive a set of parametersdefining the modified image; comparing the set of parameters derivedfrom the modified image with the set of parameters derived from the oneor more real images; determining the likelihood of additional objectsbeing present within the one or more real images; repeating the steps ofinserting, analyzing, comparing and determining; stopping the steps ofinserting, analyzing, comparing and determining when the probability ofadditional, unidentified objects being present in the one or more realimages has met a predetermined level; and generating a report describingcompiled results of repeating the steps of inserting, analyzing,comparing and determining.

Various other embodiments of the method described herein for detectingan object in an image may include obtaining one or more real images;analyzing the one or more real images and deriving a set of parametersdefining each of the one or more real images; removing at least oneobject in at least one of the one or more real images to create amodified image; analyzing the modified image to derive a set ofparameters defining the modified image; comparing the set of parametersderived from the modified image with the set of parameters derived fromthe one or more real images; determining the likelihood of additionalobjects being present within the one or more real images; repeating thesteps of removing, analyzing, comparing and determining; stopping thesteps of removing, analyzing, comparing and determining when theprobability of additional, unidentified objects being present in the oneor more real images has met a predetermined level; and generating areport describing compiled results of repeating the steps of inserting,analyzing, comparing and determining.

Still other embodiments of the invention may be directed to a systemincluding at least one detector for acquiring at least one real image; aprocessor configured to analyze images and generate a set parameters incommunication with the at least one detector; a processor configured tocreate at least one modified image by inserting at least one artificialobject into the at least one real image or removing at least one objectfrom at least one of real image in communication with the processor foranalyzing images; a processor configured to compare one or more sets ofparameters generated for the at least one real image and one or moresets of parameters generated for at least one modified image incommunication with the processor for analyzing images; a processorconfigured to determine whether enough modified images have been createdin communication with the processor for comparing sets of parameters andthe processor for creating modified images; and an output device.

In yet other embodiments, the invention described herein encompasses ainformation storage device comprising an algorithm in computer readableform for analyzing the one or more real images and generating a set ofparameters defining the one or more real images; creating a modifiedimage by inserting or removing objects in the real image; analyzing themodified image and generating a set of parameters defining the one ormore modified images; comparing the parameters defining the modifiedimage with the parameters defining the one or more real images;determining the probability of at least one additional object beingpresent in the image; determining the uncertainty in at least oneparameter; repeating the steps of inserting, analyzing, comparing anddetermining; and stopping the steps of inserting, analyzing andcomparing when it is determined that the probability of at least oneadditional object being present at one or more locations on the one ormore real images, and/or the uncertainty in the at least one parameterhas reached a threshold confidence level.

DESCRIPTION OF DRAWINGS

For a fuller understanding of the nature and advantages of the presentinvention, reference should be made to the following detaileddescription taken in connection with the accompanying drawings, inwhich:

FIG. 1 is a flow chart illustrating one embodiment of the method of theinvention in which one or more artificial images are added to an image.

FIG. 2 is a flow chart illustrating one embodiment of the method of theinvention including the removal of an object from an image.

FIG. 3 is a flow chart illustrating one embodiment of the method of theinvention including iterative comparisons of various acquired images.

FIG. 4 shows a digital satellite terrestrial image of an area includingseveral objects.

FIG. 5 shows the digital satellite terrestrial image of FIG. 4 havingseven artificial objects inserted into the image at various locations.

FIG. 6 shows an exemplary extraterrestrial image illustrating “good”data.

FIG. 7 shows an exemplary extraterrestrial image illustrating “bad”data.

FIG. 8 shows a plot of the limiting magnitude of an extraterrestrialimage versus the log of background brightness.

FIG. 9 shows a plot of the limiting magnitude of an extraterrestrialimage versus seeing.

DETAILED DESCRIPTION

It must be noted that, as used herein, and in the appended claims, thesingular forms “a”, “an” and “the” include plural reference unless thecontext clearly dictates otherwise. Unless defined otherwise, alltechnical and scientific terms used herein have the same meanings ascommonly understood by one of ordinary skill in the art. Although anymethods similar or equivalent to those described herein can be used inthe practice or testing of embodiments of the present invention, thepreferred methods are now described. All publications and referencesmentioned herein are incorporated by reference. Nothing herein is to beconstrued as an admission that the invention is not entitled to antedatesuch disclosure by virtue of prior invention.

As used herein, the term “about” means plus or minus 10% of thenumerical value of the number with which it is being used. Therefore,about 50% means in the range of 45%-55%.

“Optional” or “optionally” may be taken to mean that the subsequentlydescribed structure, event or circumstance may or may not occur, andthat the description includes instances where the event occurs andinstances where it does not.

The invention described herein is generally directed to a method foridentifying one or more objects in an image, a method for assessing theprobability of one or more unidentified objects being present in theimage and a method for assessing the quality of analysis of an image.

In various embodiments of the invention, methods for identifying objectsin an image and/or assessing the probability of the presence of anunidentified object in the image may be performed using, for example,iterative Monte Carlo-type simulation techniques. For example, FIG. 1 isa flow chart describing one embodiment of the method of the inventionincluding the steps of obtaining one or more real images (1.1),analyzing the real image (2.1) and identifying objects present in thereal image (3.1). At least one artificial object may be inserted intothe real image to create a modified image (4.1 and 5.1). In someembodiments, the at least one artificial object may be an artificialobject based on objects identified in the one or more real images (4.1),and in others, the at least one artificial object may be acquired from alibrary of objects (5.1). The modified image may than be analyzed (6.1)and various parameters derived from the modified image may be comparedwith the parameters determined from the real image (7.1). A user or apredetermined set of parameters may than be used to determine thelikelihood of additional objects being present in the image (8.1). Ifthere is a probability of additional, unidentified objects being presentin the image, one or more parameters of the artificial object may bealtered or adjusted (10.1 and 9.1). As in the previous steps, theartificial image whose parameters are altered may be based on the objectfrom the real image (10.1) or acquired from a library (9.1). Themodified artificial object may then be reinserted into the image tocreate a second modified image (12.1 and 11.1). The second modifiedimage may be analyzed using the same method as in step 6.1 or adifferent method for analyzing the artificial image may be used. Theparameters of the second modified image may then be compared to the realimage or the first modified image as in step 7.1. Differences in one ormore parameters caused by the artificial objects of the modified imagemay then be identified and their effect on the accuracy of the realimage may be determined as in step 8.1. For example, the addition of anartificial object may cause various parameters in a portion of the realimage containing an as to yet unidentified object to be altered suchthat the unidentified object becomes apparent. If additional objects maystill be present steps 10.1, 12.1, 6.1, 7.1 and 8.1 and/or steps 9.1,11.1, 6.1, 7.1 and 8.1 may be repeated any number of times until it canbe determined that the likelihood of additional unidentified objectsbeing in the real image is sufficiently low.

Real and artificial objects may be any object that may be usefullyidentified using the methods of the invention and that may be present inan image, including, but not limited to, a human; an animal; a plant; abuilding; a machine; a vehicle, such as, an automobile, a truck, a tank,a boat, a ship, or an armored personnel carrier; a military implement;an ammunition dump; an artillery installation; an encampment; a bunkerand any combinations of these or other objects. In some embodiments, thereal or artificial object may not have a discernable shape, but rathermay be a simple geometric shape having at least one opticalcharacteristic in common with any of the objects identified. Forexample, all or a portion of an artificial object meant to represent atruck may be a square having a color, reflectivity, or brightnessassociated with a real truck.

In various other embodiments of the invention, one or more objects maybe removed from the real image to create a modified image having fewerobjects. For example, in one embodiment exemplified by the flow chart ofFIG. 2, one or more real images may be acquired (1.2), analyzed (2.2)and objects in the real images may be identified (2.3), as in thepreviously described embodiment. Objects identified in the real imagesmay then be removed (4.2) to create a modified image and the modifiedimage may be reanalyzed (6.2). The parameters derived from the modifiedimage may then be compared with the parameters of the real image (7.2)to determine the likelihood of additional objects being present in theone or more real images. Iterative analysis may then continue by addingartificial objects (9.2) as described above or removing additionalobjects (10.2) to create further modified images that can be reanalyzed(6.2) and whose parameters may be compared with the one or more realimages and/or modified images (7.2). Analysis may continue until thelikelihood of additional objects being present in the real image issufficiently low (8.2), or until threshold confidence levels have beenachieved (14.2). Without wishing to be bound by theory, the removal ofreal objects from an image may allow for the resolution of additionalobjects in the image to be discerned. For example, a portion of anobject may be evident in an image but the total form of the object maybe obscured by, for example, a building, vegetation, ground-cover, ashadow or camouflage. By selectively removing a real object obscuringthe hidden object, the hidden object may come more fully into view.

In still other embodiments of the method, iterative analysis is carriedout using only real images. For example, as illustrated by the flowchart of FIG. 3, a first real image may be acquired (1.1.3), analyzed(2.1.3), and objects may be identified (3.1.3), and a second real imagemay be acquired (1.2.3), analyzed (2.2.3) and objects may be identifiedin the subsequent image. Alternatively or concurrently, a previouslyacquired image may be acquired (1.3.3), analyzed (2.3.3) and objects maybe identified (3.3.3). The parameters determined for the first realimage, the second real image and/or the previously acquired image, suchas, for example, a satellite image acquired weeks, months or yearsbefore the first real image, may be compared (4.3) and the likelihood ofunidentified objects in the first real image that may be obvious in thesecond real image or the previously acquires image may be determined(8.3). Additional images may be analyzed and compared in the same way byiteratively repeating steps 1.2.3, 2.2.3 and 3.2.3 and/or steps 1.3.3,2.3.3, and 3.3.3 and 4.3. For example, in one embodiment, satelliteimages depicting an area may be acquired once per hour for several daysto create a set of images which may be iteratively compared to oneanother to determine a confidence level that all of the objects in theimages have been identified. In another embodiment, a current real imageor a set of current real images may be compared to a stock image. Instill another embodiment, the method of the invention may includeiteratively comparing real images, as illustrated in FIG. 3, whileconcurrently inserting artificial images and/or removing objects, asillustrated in FIG. 1 and FIG. 2, from one or more of the real images orprevious images.

As used herein, the term “iteration” may refer to any combination ofsteps in the method described above including creating a modified image,analyzing the modified image and comparing the modified image to thereal image. Objects may be inserted or removed during any iteration ofthe methods described above. For example, in one embodiment, objects maybe added in several iterations followed by one or more iterations wherean object is removed from the real image. In another embodiment, severaliterations including an object being removed may be carried out followedby several iterations including the addition of objects to the realimage. In yet another embodiment, one or more iterations including theaddition of an object may be carried out followed by several iterationsincluding the removal of an object from the real image, followed byseveral more iterations including the addition of artificial objects.Iterations may proceed using any combination of iterations whereinartificial objects are added to the image and real objects and/orartificial objects are removed. In still another embodiment, artificialobjects may be added to a real image and real and/or artificial objectsmay be removed in the same iteration.

In certain embodiments, predetermined threshold confidence levels may beused to determine the likelihood of additional, unidentified objectsbeing in the real image (FIG. 1, 14.1, FIG. 2, 14.2 and FIG. 3, 14.3).In such embodiments, if threshold confidence levels have not beenreached, steps 10.1, 12.1, 6.1, 7.1 and 8.1 and/or steps 9.1, 11.1, 6.1,7.1 and 8.1 of FIG. 1, steps 10.2, 12.2, 6.2, 7.2 and 8.2 and/or steps9.2, 11.2, 6.2, 7.2 and 8.2 of FIG. 2, and/or steps 1.2.3, 2.2.3, 3.2.3and 4.3 and/or steps 1.3.3, 2.3.3, 3.3.3 and 4.3 of FIG. 3 may berepeated until the confidence level thresholds have been satisfied. Onceit has been determined that there is a low likelihood of additional,unidentified objects being present in the real image or thresholdconfidence levels been reached, a report may be generated that detailsthe analysis of the image (FIG. 1, 15.1, FIG. 2, 15.2 or FIG. 3, 15.3).

Hereinafter, a “known data set” shall be defined as a set of parametersincluding any number of data values describing a real image. An “unknowndata set” shall be defined as a set of parameters describing a modifiedimage and may be acquired by inserting or removing any number of objectsinto or out of a real image to create a modified image and reanalyzingthe modified image to produce a set of parameters defining the modifiedimage. In various embodiments of the invention, comparing a known dataset and an unknown data set may be used to identify parameters that havealtered as a result of the insertion of an artificial object or theremoval of a real object from the image. Without wishing to be bound bytheory, alterations in parameters may allow for the identification ofareas within the real image where additional, unidentified objects maybe present.

The known and unknown data set may contain data values for any number ofparameters useful in various embodiments of the invention to define thereal or modified image or a portion of the real or modified image. Forexample, in some embodiments, a known or unknown data set may includedata values including a number of objects in the image, the brightness,contrast, color, shape, orientation or location of the identifiedobjects or the brightness contrast, color and so on of any portion ofthe image that may or may not contain an object. During iterativeanalysis an unknown data set may be obtained by analyzing the modifiedimage acquired in each step including the insertion of one or moreartificial objects into an image or removal of one or more real objectsfrom the image, and artificial objects may by inserted into a real ormodified image at any number of positions in the image or objects may beremoved from a real or modified image any number of times duringiterative analysis to create a plurality of unknown data sets.Therefore, following iterative analysis a large number of data valuesmay have been generated for each of the parameters describing the realimage which may be compared and statistically analyzed using methodsknown in the art.

Without wishing to be bound by theory, the uncertainty associated withany number of the parameters identified may be determined by comparing anumber of parameters determined for any number of real and/or modifiedimages. Additionally, this uncertainty may be minimized by providing agreater number of measured values for each parameter which can beachieved by performing a greater number of iterations or by continuingiterative analysis. By minimizing the uncertainty associated with eachparameter, the statistical error associated with each parameter may bereduced and/or minimized by performing iterative analysis, thecompleteness of analysis of the image may be quantified and theconfidence level of these parameters may be ascertained. In short, overa number iterations, the error can be reduced and the likelihood of anobject being present in that location may be ascertained. When theuncertainty of each parameter has been effectively minimized, theaverage of each parameter may be deduced to provide a “final data set”and the real image may be reevaluated using the final data set.Reevaluation may allow for objects which were not identified in theinitial analysis of the image to come into greater resolution therebyallowing the previously unidentified object to be identified.

In another embodiment, Monte Carlo simulations may be used tosystematically probe the real image throughout its field of view. Forexample, in one embodiment of the invention, a portion of an image maybe identified during iterative analysis which has a high likelihood ofcontaining a previously unidentified object. In such embodiments, thisportion of the image may be isolated and iterative analysis may becarried out specifically on this portion of the image. For example, oneor more objects may be inserted into the identified portion of the imagewhile altering various parameters of the object, such as, theorientation, brightness, contrast, or color until the uncertainty of anobject in this portion of image has been effectively minimized.

Careful interpretation of the differences between a known data set andan unknown data set may provide a wealth of information. For example, inone embodiment, iterative analysis may derive empirical uncertainties oftarget properties, including, but not limited to, opticalcharacteristics such as, brightness, reflectivity and color of anobject, location of the object, displacement/velocity of the object andso on and the probability of detection for a wide range of targetparameters. The confidence levels generated by embodiments of theinvention may provide a more accurate estimate of the number of actualobservable objects that have been identified as well as an estimate ofthe number of objects that have been potentially missed. Moreover,embodiments of the invention may provide repeatable, quantitativemeasurements of the various properties of an image and theiruncertainties.

In some embodiments, the method of the invention may further include thestep of recovering an optical characteristic based on the results ofiterative analysis of an image and utilizing the recovered opticalcharacteristic in further analyzing the image or in analyzing similarimages. As used herein the term “recovered optical characteristic” or“recovered parameter” refers to an optical characteristic or data valueassociated with a specific parameter determined through iterativeanalysis. The recovered optical characteristic may be associated withany parameter, including, but not limited to, brightness, color,location, orientation, reflectivity or combinations thereof.Additionally, in certain embodiments, the recovered parameters mayencompass a probability of recovering a parameter using iterativeanalysis or uncertainties regarding a parameter or uncertaintiesregarding any aspect of a recovered parameter. In such embodiments, arecovered optical characteristic may be determined to be significantlydifferent than the initially measured data value for a parameterassociated with the optical characteristic for all or a portion of theimage under study, and the data value for the recovered parameter may beused to adjust this parameter in other portions of the image under studyand/or different subsequent images of the same or a similar area. Forexample, the brightness of a portion of an image may be determinedthrough iterative analysis to be significantly lower than the brightnessvalue initially measured due to, for example, a reflection. The datavalue for the recovered brightness may then be used to adjust thebrightness in other portions of the image thereby reducing the effect ofthe reflection throughout the image allowing a source of the brightnessto be revealed.

In various embodiments, the method described hereinabove may beautomated. For example, in some embodiments, the steps for successiverounds of iterative image analysis may be automated, and in severalembodiments, the automated steps may include determining if additionalobjects are present in the real image. For example, in some embodiments,a rules-based system may be used for determining when iterative roundsof inserting artificial objects or removing real objects from one ormore images have been carried out an adequate number of times to achievethe desired result, such as, reducing the uncertainty of a number ofparameters a sufficient amount. In at least one embodiment, such arules-based system may include a statistical framework to evaluate adegree of statistical confidence that an unidentified object is presentin an image or a portion of an image. Without wishing to be bound bytheory, by developing a rules-based system for determining the number ofsuccessive iterations that are adequate, the majority of work mayproceed automatically at computational speeds until a final desiredresult has been achieved and the amount of human interaction requiredfor the decision making process of on-going evaluations may be reducedthereby reducing the total time required to achieve the desired result.

As described above, some embodiments of the invention may include thegeneration of a report detailing the results of iterative analysis ofthe image (See, for example, FIG. 1, 15.1, FIG. 2, 15.2 or FIG. 3,15.3). The parameters reported in the report may vary among embodimentsof the invention and may include any number of observed, measured,calculated or statistically-derived (for example, average, median, etc.)parameters. Additionally, the report may contain results associated withstatistical analysis of the results of iterative analysis and mayreflect the completeness of analysis of the image. For example, in oneexemplary embodiment, the report may simply provide the location andorientation of all the identified objects in the image understudy and ameasure of completeness of analysis, for example, the analysis is 95%complete indicating that there is a 5% likelihood that additionalunobserved objects are present in the image. In another exemplaryembodiment, the report generated may contain an initial number ofobjects identified, an estimate of a number of objects identifiedthrough iterative analysis, the average of the identified objects andunidentified objects, the error associated with this average number ofobjects, and the confidence level that the iterative analysis of thereal image has been carried out a sufficient number of times. In anotherexample, it may be concluded that if an object were present in a realimage, it would have been detected, for example, 95% of the time. Inanother embodiment, the report may contain averages for any or all ofthe parameters measured during iterative analysis for the entire imageor any portion of the image. For example, an average brightness,contrast, color, orientation, or location for an object may be reportedalong with errors associated with each real or modified image and anoverall confidence level for the average measurements. As used herein,the term “confidence level” may, generally, refer to the degree of errorassociated with the iterative analysis or a data value determined as aresult of such analysis. For example, a “confidence level” for aparticular parameter's uncertainty may be reported as 0.005%+/−0.001%indicating that the “uncertainty in the uncertainty” has been measuredfairly precisely. In yet another embodiment, information obtained usingmethods embodied herein may be used to determine the 3-sigma upper limit(3-standard deviation limit) to how many people might be present butunseen given a detected group of a certain size. For example, anestimated population of an area may be ascertained by analysis of anumber of vehicles, a size of various buildings within the field ofview, and so on.

Conclusions such as those provided by iterative analysis may provideadditional information regarding the likelihood of objects being presentin an image which may provide a greater degree of confidence in decisionmaking. For example, in one embodiment the information extracted via thetechniques embodied herein may allow for an increase in actionableintelligence and real-time assessment of the probability of detectionfor a set of objects of interest. In a further embodiment, analysesprovided by the methods embodied by the invention may be applied in theacquisition and analysis of specific targets. Additionally, theconclusions reached by iterative analysis may be used to automate theanalysis of other images or to prioritize or triage similar images andto provide an estimate of the completeness of analysis of other images.Without wishing to be bound by theory, information obtained fromanalysis of images that have been compiled, may be utilized to improveconsistency of analysis over a broader range of images.

The report may be in any format known in the art and may be provided onany medium. For example, a report may be formatted as a list orspreadsheet and may be provided on any medium, including, but notlimited to, paper, computer monitor, a digital recording device, or anycombination thereof.

The images utilized in the methods described herein may be from anysource. For example, in some embodiments, images utilized may be derivedfrom an analog or digital camera or image detector and the images may bestill images or video images or if more than one images are utilized inobject analysis the various images may be acquired from a combination ofstill or video images. In other embodiments, the images utilized inmethods of the invention may be non-optical in nature. For example, theimages may be derived from an infrared (IR) sensor or detector, anultraviolet (UV) sensor or detector, radar sensor or detector, syntheticaperture radar (SAR) sensor or detector, sonar sensor or detector andthe like, or a combination thereof. The camera, image detector, ornon-optical detector of various embodiments may be fixed or movable, forexample, an image detector or IR detector may be handheld, mounted on abuilding or mounted on a motorized vehicle such as, for example, a car,truck, tank, boat, ship, airplane, spaceship or a satellite. In stillother embodiments, the detector may be mounted on a device, such as, forexample, telescope or magnifying or telephoto device. As used herein theterm “detector” may refer to an optical image detector or camera, or anon-optical sensor or detector, such as, but not limited to an IR or UV,radar, SAR and sonar sensors or detectors.

Any image may be analyzed using the method of the invention. As usedherein, the term “image” shall refer to an image acquired from anoptical imager, such as, a camera or image detector, or a non-opticalimage acquired from, for example, a IR detector or radar. For example,in some embodiments, an image may include complex, crowded fields, suchas extraterrestrial images of stars and/or planets acquired from atelescope, terrestrial images acquired from satellites, a fixed ormovable detector or a handheld device including a large number ofobjects. The images of embodiments may be of an area whose size may varydepending on the range and capability of the detector used and mayencompass a terrestrial area, an aquatic area, an extraterrestrial areaor any combination thereof. In various embodiments, images may be astill or video image encompassing aerial images, satellite, ground toground images, ground to air images, ground to water images, water toground images, water to water images, air to air images, or acombinations of these. For example, an aerial image acquired from adetector mounted on a satellite or airplane may encompass a largeterrestrial area with a portion of the image including an aquatic area,such as, a portion of a lake, ocean, river, pond, pool, marsh, swamp, orcombination thereof. In another example, the ground to ground imageacquired from a detector mounted on a truck and may encompass a largeterrestrial area with a portion of the image including anextraterrestrial area, such as the sky. In yet another example, a waterto water image acquired from an detector mounted on a ship may encompassa large aquatic area with a portion of the image sky and/or ground.

Images acquired may include any number of observable objects, such as,for example, buildings, structures, trees, plants, geologicalformations, aquatic features, aerial features, extraterrestrialfeatures, vehicles, such as, cars, trucks, tanks, boats, ships,airplanes, military implements, artillery installations, animals,humans, or combinations thereof, and any of the preceding objects may bewholly or partially covered or hidden in the acquired images. In someembodiments, the specific type of object may be determined. For example,a type of building, such as, a storage building, barracks, power plantor type of plant or a type of vehicle, such as, the type of airplane,type of car or truck, type of ship, etc. may be determined from a realimage. The images acquired in various embodiments may also contain anynumber of unobservable or hidden objects, and such unobservable orhidden objects may be the same or different from the observable objects.

The method of some embodiments of the invention may further include thestep of enhancing the one or more images. Enhancing may include anynumber or combination of steps for the purpose of making the image as awhole or one or more objects in the image more clearly visible. Forexample, enhancing may include the steps of performing geometrictransformations, such as, enlarging, reducing, or rotating the image ora portion of the image; adjusting the color of the image by, forexample, adjusting brightness and contrast, quantizing the image,histogram matching or converting to a different color space, such as,for example, gray scaling; registering or aligning two or more images;combining or splicing of two or more images using, for example,averaging, sigma clip averaging, registering, median-filtered combining,coadding, blending, differencing, or creating a composite image;collaging; convolution or deconvolution filtering; unsharp masking; edgedetecting; performing a Fourier transformation; reducing backgroundnoise; texturing; mosaicing; interpolating; demosaicing, and/orrecovering a full image from a raw image data or format; segmenting theimage into regions, editing or retouching the image; extending thedynamic range of an image by combining images taken under differentexposures; restoring an image by, for example, deconvoluting to reduceblur, restoring of faded color, removing of scratches; and anycombination thereof.

In certain embodiments, images from different bands may be combined. Forexample, in one embodiment, an optical image may be aligned, combined,averaged, blended and so forth with a non-optical image, such as, an IRimage (combined optical and IR data are referred to as OIR) to producewhat may be referred to as multispectral data. In another embodiment,radically different bands from different sensors or detectors may becombined in a process referred to as sensor fusion to producehyperspectral data. For example, hyperspectral data may be derived bycombining optical and SAR images and SAR and OIR data. In embodimentsusing multispectral or hyperspectral data, one or more iterations mayinclude the step of acquiring data from more than one band, for example,optical and IR or SAR data.

The methods of various embodiments of the invention may include anynumber of enhancing steps and enhancing steps may occur at any pointthroughout the analysis process described herein. Additionally, all ofthe image or only a portion of the image may be enhanced during anyenhancing step. For example, a terrestrial image acquired from asatellite may be enhanced by reducing background noise, adjusting thebrightness and contrast, and performing edge detection for the entireimage. The image may then be analyzed and a portion of the image may beenhanced by enlarging an area of the image containing, for example, astructure and deconvoluting that portion of the image. Analysis maycontinue and a separate area of the original image may be enhanced todistinguish a previously unobserved structure.

Some embodiments of the invention may additionally include the step ofdigitizing the acquired images. Digitizing generally refers to theprocess of converting an image into a digital or computer readable andcomputer manipulatable format. The step of digitizing is not limited toany specific method or mode of digitizing an image and may occur by anymeans known in the art. For example, in one embodiment, an acquiredimage is digitized automatically. Many digitizing software packages areknown and used in the art. Any of these may be used to digitize theacquired images of various embodiments of the invention. In anotherembodiment, digitizing occurs manually wherein a user uses a graphicalinterface, a cursor and a digitizing table to generate a digitizedversion of the image.

Each analyzing step may be carried out by any method known in the art.For example, in one embodiment, the first analyzing step may beaccomplished by a human user who visually detects and reports one ormore objects in a real image. For example, the user may locateobservable objects, such as, buildings, vehicles, humans, and the like,and provide a location for these objects in a digital grid which isutilized in an automated process. In another embodiment, AutomaticTarget Recognition (ATR) software is used to detect objects in an image.Briefly, in such embodiments, the acquired image and modified images maybe digitized and divided into a plurality of image chips each image chipincluding a portion of the acquired image. The image chips may then beparsed into chips having a high probability of containing objects orportions of objects and chips having a low probability of containing anobject. In still other embodiments, a real image may be divided into aplurality of image chips and each of the image chips may be individuallyanalyzed using the iterative method of the invention. In yet otherembodiments, a real image may be divided into a plurality of image chipsand only those image chips having a high probability of containingobjects may be analyzed using the iterative analysis of the invention,and in further embodiments, iterative analysis may be performed on aregion of an image where the greatest level of parameter precision isrequired. Similarly, parameters may be adjusted for individual imagechips or for the acquired or modified image as a whole. In embodimentswherein image chips are processed individually, the acquired image maybe reconstructed following analysis of each iteration, or the image maybe reconstructed at the completion of a complete iterative analysis. Insome embodiments, astronomical algorithms may be utilized to assessterrestrial images because the low signal-to-noise ratio hyperspectraldata of unresolved celestial targets exactly minors the lowsignal-to-noise ratio hyperspectral data of unresolved terrestrial dataprovided by satellites.

The step of inserting artificial objects into the real image may becarried out using any method known in the art or available to the user,for example X-Patch, Paint-The-Night (PTN) and Night Vision andElectronics Sensors Directorate Electro-optics Simulation Toolkit(NVEOST). Additionally, several software packages have been recentlydeveloped to insert artificial stars into a astronomical imageincluding, but not limited to IRAF and SPS. While these packages werenot designed specifically for use with terrestrial images, they may beuseful in iterative analysis of terrestrial images or may be modified tosuccessfully prepare a package for analysis of terrestrial images.Additionally, these or similar packages may be modified to assess theconfidence limits for a real image, given the results of iterativeanalysis. For example, it may be efficient to use a routine similar toPTN or NVEOST to embed an image chip of an artificial object into a realimage in order to make the most accurate hyper-spectral model of, forexample, a tank under sand, netting, and/or trees. It should be noted,the insertion of artificial objects into a real image is meant to probethe image and not just to create a new image.

The artificial objects inserted into an image may be derived from anysource. For example, in some embodiments, the artificial object insertedinto an image may be derived from a library of stock or archived objectsof interest. For example, artificial objects inserted into a terrestrialimage of a military encampment may include, but not be limited to,vehicles, such as, trucks, tanks, missile launchers, aircraft,motorcycles, boats, ships and the like; buildings, such as, houses,barracks, garages, storage buildings, weapons depots, and the like;people; armaments, such as, anti-aircraft artillery, missiles, and soon. In some embodiments, artificial objects from a library that areinserted into an image may be roughly defined including only an outlineof an object or a simple geometric shape having a color or contrast. Inother embodiments, the artificial objects may be extremely well definedin near-analytical terms, including, for example, a well definedgeometry, albedo, spectroscopic properties, polarimetric properties, andthe like. In such embodiments, a caveat to the three-dimensional libraryobject may be the insertion geometry versus the image acquisitiongeometry. However, this transformation is well known in the art androutine to the skilled artisan.

In another embodiment, the artificial objects inserted into an image maybe based on real objects observed in the real image. For example, avehicle observed in the real image may be used to probe the image foradditional, unobserved vehicles in the image. In such embodiments, asignature associated with the identified object may be extracted fromthe data derived from the initial analysis of the real image, and thissignature may be used to probe the image for additional, unobservedobjects. In another embodiment, the signature of a portion of anidentified object may be used to probe an image where the only examplesof an observed object available are at least partially obscured. In yetanother embodiment, the signature of an at least partially obscuredobject may be used to assess detectability of other at least partiallyobscured objects, and this assessment may be used to evaluate thelikelihood of additional objects being present but at least partiallyobscured. The extraction of an observed object may be accomplished byany method known in the art. For example, extraction of an object may bebased upon an area selection tool which may be manual or automatic.Additionally, the selection tool may additionally include an optional“auto-grow” tool wherein partially obscured objects with clearlyidentifiable edges are compared to archived objects and edge lines areadded to complete the object. In still another embodiment, an object maybe fabricated in a real image at a position where an unobserved objectis known to exist or a portion of the image having a high likelihood ofan unobserved object being present. In yet another embodiment, imageanalysts may be used to build a library based on object extracted fromreal images.

Artificial objects selected from a library or based on an identifiedobject may be additionally manipulated by, for example, rotating,scaling, shearing, smearing, or otherwise degrading the artificialobject such that the object is consistent within the context of the realimage. For example, in some embodiment, artificial objects mayadditionally include shadows, obscuration, layover, multipath, indirectilluminations, and reduced/partial transparency effects thereby allowingthe artificial object to appear more realistic in the environment of theimage, and in one embodiment, these aspects of the artificial object maybe altered or adjusted based on, for example, the time period in whichthe image was acquired. For example, the shadow associated with anartificial object may be adjusted based on whether a terrestrial imagewas acquired in the morning, afternoon, evening or at night. In at leastone embodiment, illumination angles and shadow properties may begenerated from ambient features of the real image, such as,ground-plane, trees, buildings, etc. which may be used to add realisticshadows to artificial objects. In another embodiment, an artificialobject may be rotated, sheared, smeared, convoluted, degraded, or scaledto fit within the context of an image and corresponding shadows may beadded to the artificial object to fit the time frame in which the imagewas acquired. In yet another embodiment, an artificial object may beobscured by adding, for example, a known camouflage pattern, simulatednetting, simulated vegetation, simulated ground covering, simulatedwater covering, simulated cloud covering, simulated weather, convolvingwith a nearby object or combinations thereof. Thus, a partially obscuredobject may also be simulated. In still another embodiment, a ray-tracingor a ray-casting code-base may be utilized to insert artificial objects.For example, a computer program, such as, the open source POV package,may be used to define the ground-plane (or planes) with contextualclicks and foreground object categories (trees, buildings, etc.) thatcan obscure the artificial object. The transparency, seeing, signal tonoise ratio, and so on may also be selected using this approach.

In yet another embodiment, the process for inserting artificial objectsmay include a noise module wherein shot-noise, detector noise, anddegradation due to optics and atmospheric effects may be added to theartificial object. In addition, other noise components may be added tothe noise module, such as, but not limited to, diffuse reflection orblending from nearby objects and light sources. Noise may be applied toan artificial object when or where the artificial object is insertedinto the image and the noise may become part of the signature in thatartificial object. The specific approach for adding noise may dependupon whether an archived object from a library or a recovered/extractedobject from a real image is inserted or reinserted in a different place.In embodiments wherein recovered real objects are reinserted into animage, some parts of the noise model may not be reapplied, for example,PSF degradation and/or detector noise may not be applied, although shotnoise may have to be modified due to illumination and/or scaling. Ineither instance, reflection effects can be important considerations. Insuch embodiments, transparency, atmospheric variables, noise and thelike may be resolved by comparison to observable objects in the realimage, such as a static buildings, land features, etc.

In various embodiments objects identified by iterative analysis mayrequire some degree of human characterization. For example, in oneembodiment, the method of the invention may include the step ofcomparing an identified object to various objects in a library todetermine the type of object identified. In another embodiment, anauxiliary procedure may be provided to estimate key geometric propertiesof the identified object. For example, the observed geometry of anidentified object and it's viewing parameters, such as, for example,altitude, azimuth, pixel-scale and observed instrumental targetdimensions may provide quantitative information which may be used toidentify an unobstructed object from a library of objects.

The method of embodiments described herein are not limited by theapparatuses used for their implementation. For example, in oneembodiment, the method may be encoded by an algorithm or computerprogram in computer readable form. Such an algorithm or computer programmay be encoded onto any information storage device known in the art,including, but not limited to, a computer readable disk, a CD, a DVD, ahard drive, a flash drive, an optical drive, a zip disk and a tape. Thealgorithm or computer program may be utilized by any machine capable ofdecoding and implicating the algorithm or computer program for theanalysis of an image. Such machines include, but are not limited to,computers, computer processors, image processors and so on.

The invention described herein further includes systems for implementingthe method and embodiments of the invention are not limited by the typeof system utilized. For example, in one embodiment, the system mayinclude a detector for acquiring real images, a processor for digitizingthe real image in communication with the detector, a processor foranalyzing the real image in communication with the processor fordigitizing the image, a processor for creating modified images byinserting artificial objects into the image in communication with theprocessor for analyzing the image, a processor for analyzing themodified image in communication with the processor for creating modifiedimages, a processor for comparing the results of analysis of the realimage and the modified image in communication with the processor foranalyzing the real image and the processor for analyzing the modifiedimage, a processor for determining whether enough iterations haveoccurred in communication with the processor for comparing and theprocessor for creating modified images, a device for generating a reportin communication with the processor for determining whether enoughiterations have occurred, and an output device. The type of processorsutilized in such systems may vary among embodiments and may bemechanical devices, such as, computer processors, image processors, andso on or the processors may be one or more humans. In some embodiments,each of the processors described above may be a single processorperforming the steps of the methods of the invention in an order suchthat iterative analysis may be accomplished. In other embodiments, acomputer processor may complete a number of steps in the method, and ahuman processor may perform other steps in the process. In still otherembodiments, a computer processor may be capable of completing each stepin the processor, but a human may intervene by performing any number ofsteps in combination with the computer processor or in place of thecomputer processor.

This invention and embodiments illustrating the method and materialsused may be further understood by reference to the followingnon-limiting examples.

Example 1

FIG. 4 is a DigitalGlobe satellite image of a facility including a largebuilding at the center, probable vehicles to the right of the buildingand an object to the left of the building (circled). Initial analysis ofthe image concludes that the object to the left of the building is anemplacement of an anti-aircraft artillery (AAA) battery. To assess thelikelihood of additional unobserved AAA emplacements being present inthe image, additional objects having similar visual properties to theidentified AAA emplacement have been added to FIG. 5. In FIG. 5, seveninstances of the AAA emplacement noted in the previous figure have beenadded (circled). Subsequent reanalysis of the image of FIG. 5 may, forexample, recover six instances of this particular object of interest.After repeating 100 iterations of similar analysis, it may be concludedthat one would expect to observe 85% of such AAA emplacements based uponrepeated reanalysis over the full terrain covered in the images field ofview. The number of repeat experiments may also determine the confidencelevel of the analysis, with more experiments producing greatercertainty, at the cost of time and computational resources. Of course,in a real example, the background would be matched and the correct noisemodel would be employed, using, for example, Paint-the-Night, NVEOST orX-Patch. While numerous subtleties exist, this example provides theconceptual framework for the methods of the invention.

Example 2

To demonstrate the automated approach to the problem of completeness anduncertainty estimation, Artificial Star Simulation and Modeling Tool(ArtStar) toolkit on a set of 31 frames of the field of the X-ray binaryGRO J1655-40 in the Galactic plane was tested using iterative imageanalysis. Real images are provided in FIG. 6 and FIG. 7. In theseimages, the field of view is mildly crowded, and the major limitation onthe completeness is the resolution of the objects (stars), thebrightness of the background, and the degree of cloudiness duringacquisition of the images.

FIG. 6 is an example image collected of X-ray binary GRO J1655-40,located in the Galactic disk and represents “good” data. Although thisimage is of high quality, the level of crowding is significant due tothe very high density of stars in the image. The stars in this imagelook bigger than those in FIG. 7 only because of the display contrastused. FIG. 7 is another example image of GRO J1655-40 and represents“bad”, cloudy data. This exposure is a lower quality image withsignal-to-noise being lower and image resolution being worse. It shouldbe noted that the ability to see faint objects in the “bad” image (FIG.7) is significantly curtailed compared to the “good” data (FIG. 6).While this qualitative statement is intuitively obvious, using methodsof the invention it may be possible to quantify how much better oneimage is over another.

FIG. 8 is a plot showing that as the sky brightness level increases,only progressively brighter stars are visible. In a terrestrialapplication, this example might correspond either to a succession ofimages at different light levels or successive images effected byweather. FIG. 8 shows that the faintest detectable objects, “limitingmagnitude”, for each frame as a function of the logarithm of thebrightness of the background. “Magnitude” as used herein is a unit ofbrightness commonly used in astronomy; larger magnitudes correspond tofainter objects. The limiting magnitude of the data provided has beenestimated as having a completeness of approximately 50%. Since largermagnitudes indicate fainter stars, the frames that measure the fainteststars are the ones with the highest limiting magnitude. The mean valueof the group at the bottom is 24.93 with a standard deviation of 0.04magnitudes, better than the targeted error of 0.05.

FIG. 9 shows a plot of the same limiting magnitude versus the seeing.The limiting magnitude shows little dependence on the seeing, but showsa stronger dependence on the level of background noise. For example,frames affected by moonlight have brighter limiting magnitudes than thebalance. The outliers with poor limiting magnitude are, therefore, theresult of clouds, which reduce the signal to noise ratio of images withconstant exposure time. Additionally, the plot of FIG. 9 demonstratesthat as atmospheric blurring increases, only progressively brighterstars are visible. Just as atmospheric conditions can reduce theresolution of astronomical images, they can degrade terrestrial imagestaken through turbulent air. Since image resolution can be a keyparameter for detecting objects of interest, this type of assessment canbe a critical tool to help triage the masses of unmanaged imagery datathat arrives hourly.

Taken together, the data provided in FIG. 8 and FIG. 9 enable the choiceof which images are the most useful. Acquiring similar sets of data forterrestrial images using the methods of the invention may enablequantitative analysis of how reliable acquired image data are and howfaint, small or obscured an object can be and still be detectable.

It will be appreciated that various of the above-disclosed and otherfeatures and functions, or alternatives thereof, may be desirablycombined into many other different systems or applications. Also,various presently unforeseen or unanticipated alternatives,modifications, variations or improvements therein may be subsequentlymade by those skilled in the art which are also intended to beencompassed by the following claims.

What is claimed is:
 1. A method for quantitative analysis of imagecharacteristics, the method comprising: assessing, by a processor,quality of an acquired image; inserting, by a processor, at least oneartificial object selected from a library into the acquired image tocreate a modified image; assessing, by a processor, quality of themodified image; determining, by a processor, a confidence level that allobjects in the acquired image have been identified; and quantifying, bya processor, reliability of the acquired image.
 2. The method of claim1, wherein the confidence level is a probability of a number of objectsin the acquired images being identified.
 3. The method of claim 1,wherein assessing the quality of each of the acquired image and themodified image comprises deriving, by a processor, a set of parameters,wherein each parameter of each set of parameters is selected from thegroup consisting of brightness, contrast, color, shape, orientation, andlocation.
 4. The method of claim 1, wherein assessing the quality ofeach of the acquired image and modified image comprises performing, by aprocessor, Monte Carlo simulation.
 5. The method of claim 1, furthercomprising determining, by a processor, whether to acquire additionalimages based on the reliability of the acquired image.
 6. The method ofclaim 1, wherein the method is automated.
 7. The method of claim 1,further comprising: assessing, by a processor, the quality of one ormore additional acquired images; inserting, by a processor, at least oneartificial object selected from a library into at least one of the oneor more additional images to create additional modified images;assessing, by a processor, the quality of each of the additionalmodified images; determining, by a processor, a confidence level thatall objects in the additional acquired images have been identified; andquantifying, by a processor, reliability of the acquired images.
 8. Themethod of claim 1, further comprising generating, by a processor, areport.
 9. The method of claim 1, wherein the at least one artificialobject is selected from the group consisting of vehicles, trucks, tanks,missile launchers, aircraft, motorcycles, boats, ships, aquaticfeatures, aerial features, geological formations, buildings, houses,barracks, garages, storage buildings, weapons depots, people, armaments,anti-aircraft artillery, missiles, trees, plants, an outline of anobject, a simple geometric shape, and combinations and portions thereof.10. The method of claim 1, wherein the at least one artificial objecthas well-defined geometry, albedo, spectroscopic properties,polarimetric properties, and combinations thereof.
 11. The method ofclaim 1, wherein the at least one artificial object is wholly orpartially covered or hidden.
 12. The method of claim 1, furthercomprising manipulating, by a processor, the artificial object.
 13. Themethod of claim 12, wherein manipulating comprises rotating, scaling,shearing, smearing, degrading, introducing shadows, introducingobscuration, introducing layover, introducing multipath, introducingindirect illumination, introducing reduced/partial transparency effects,simulating camouflage, simulating netting, simulating vegetation,simulating ground covering, simulating water covering, simulating cloudcovering, simulating weather, convolving with a nearby object, andcombinations thereof.
 14. The method of claim 1, wherein the at leastone artificial object comprises one or more optical characteristicsselected from the group consisting of one or more of brightness, color,location, orientation, texture, and reflectivity.
 15. The method ofclaim 1, wherein the acquired image is selected from the groupconsisting of aerial still images, aerial video images, satellite stillimages, satellite video images, ground to ground still images, ground toground video images, ground to air still images, ground to air videoimages, ground to water still images, ground to water video images,water to ground still images, water to ground video images, water towater still images, water to water video images, air to air stillimages, and air to air video images.
 16. The method of claim 1, whereinan area encompassed by the acquired image is selected from one or moreof a terrestrial area, an aquatic area, an aerial area, and anextraterrestrial area.
 17. The method of claim 1, further comprisingdigitizing, by a processor, the acquired image.
 18. The method of claim1, further comprising: creating, by a processor, a plurality of imagechips, wherein each image chip includes a portion of the acquired image;and parsing, by a processor, the plurality of image chips into one ormore groups of image chips having objects and one or more groups ofimage chips not having objects.
 19. The method of claim 1, furthercomprising enhancing, by a processor, at least a portion of the acquiredimage, the modified image or combinations thereof by a method selectedfrom the group consisting of adjusting contrast, adjusting color,extracting an image, collaging, registering, coadding, averaging,median-filter combining, sigma-clip averaging, splicing, histogrammatching, mosaicking, convolution filtering, deconvolution filtering,unsharp-masking, edge detecting, Fourier transforming, reducingbackground noise, texture processing, creating multispectral data, usingmultispectral data, creating hyperspectral data, and using hyperspectraldata, and combinations thereof.
 20. A system comprising: a processor;and a non-transitory, processor-readable storage medium in communicationwith the processor, wherein the non-transitory, processor-readablestorage medium comprises one or more programming instructions that, whenexecuted, cause the processor to: assess quality of an acquired image;insert at least one artificial object selected from a library into theacquired image to create a modified image; assess quality of themodified image; determine a confidence level that all objects in theacquired image have been identified; and quantify reliability of theacquired image.
 21. The system of claim 20, wherein the confidence levelis a probability of a number of objects in the one or more real imagesbeing identified.
 22. The system of claim 20, wherein instructions that,when executed, cause the processor to assess the quality of each of theacquired image and modified image comprise instructions that, whenexecuted, cause the processor to derive a set of parameters related toeach of the acquired image and modified image, wherein each parameter ofeach set of parameters is selected from the group consisting ofbrightness, contrast, color, shape, orientation, and location.
 23. Thesystem of claim 20, wherein instructions that, when executed, cause theprocessor to assess the quality of each of the acquired image andmodified image comprises instructions for carrying out a Monte Carlosimulation.
 24. The system of claim 20, wherein the non-transitory,processor-readable storage medium further comprises instructions that,when executed, cause the processor to determine whether to acquireadditional images based on the reliability of the acquired image. 25.The system of claim 20, wherein the non-transitory, processor-readablestorage medium further comprises instructions that, when executed, causethe processor to generate a report.
 26. The system of claim 20, whereinthe non-transitory, processor-readable storage medium further comprisesinstructions that, when executed, cause the processor to manipulate theartificial object.
 27. The system of claim 26, wherein manipulatingcomprises rotating, scaling, shearing, smearing, degrading, introducingshadows, introducing obscuration, introducing layover, introducingmultipath, introducing indirect illumination, introducingreduced/partial transparency effects, simulating camouflage, simulatingnetting, simulating vegetation, simulating ground covering, simulatingwater covering, simulating cloud covering, simulating weather,convolving with a nearby object, and combinations thereof.